


'''simple docstring'''


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								_SCREAMING_SNAKE_CASE					:			int							= flax_key_tuple[:-1] + ("""weight""",)
								_SCREAMING_SNAKE_CASE					:			Tuple							= torch.permute(SCREAMING_SNAKE_CASE__				, (0, 2, 1)	)
				elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE__	):
								# linear layer
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= flax_key_tuple[:-1] + ("""weight""",)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= flax_key_tuple[:-1] + ("""weight""",)

				return flax_key_tuple, flax_tensor







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if "metadata" in layer:
								_SCREAMING_SNAKE_CASE					:			Any							= layer.split("""metadata"""	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= """""".join(split_layer[0]	)[:-1]
								_SCREAMING_SNAKE_CASE					:			Dict							= [tuple(("""metadata""" + split_layer[1]).split("""/"""	)	)]
				elif "kvstore" in layer:
								_SCREAMING_SNAKE_CASE					:			List[str]							= layer.split("""kvstore"""	)
								_SCREAMING_SNAKE_CASE					:			Any							= """""".join(split_layer[0]	)[:-1]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [tuple(("""kvstore""" + split_layer[1]).split("""/"""	)	)]

				else:
								_SCREAMING_SNAKE_CASE					:			int							= layer.split("""/"""	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= """/""".join(split_layer[:-1]	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= (split_layer[-1],)

				if "kvstore/path" in layer:
								_SCREAMING_SNAKE_CASE					:			List[str]							= f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
				elif "kvstore/driver" in layer:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= """file"""
				else:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= rename_keys(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= {}
				for k, v in current_block.items():
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= v
				_SCREAMING_SNAKE_CASE					:			Tuple							= new_current_block
				torch.save(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= convert_file_size_to_int(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= []
				_SCREAMING_SNAKE_CASE					:			List[Any]							= {}
				_SCREAMING_SNAKE_CASE					:			List[str]							= 0
				_SCREAMING_SNAKE_CASE					:			Dict							= 0

				os.makedirs(SCREAMING_SNAKE_CASE__				, exist_ok=SCREAMING_SNAKE_CASE__	)
				with gfile.GFile(switch_checkpoint_path + """/checkpoint"""				, """rb"""	) as fp:
								_SCREAMING_SNAKE_CASE					:			Dict							= serialization.msgpack_restore(fp.read()	)["""optimizer"""]["""target"""]
								_SCREAMING_SNAKE_CASE					:			Dict							= flatten_dict(SCREAMING_SNAKE_CASE__				, sep="""/"""	)

				_SCREAMING_SNAKE_CASE					:			Any							= {}
				for layer in checkpoint_info.keys():
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			int							= get_key_and_tensorstore_dict(
								    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								if curr_real_layer_name in all_layers:
												_SCREAMING_SNAKE_CASE					:			int							= content
								else:
												_SCREAMING_SNAKE_CASE					:			Dict							= {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								_SCREAMING_SNAKE_CASE					:			List[Any]							= ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
								_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.tensor(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

								# use the renaming pattern from the small conversion scripts
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= rename_base_flax_keys(tuple(key.split("""/"""	)	)				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """/""".join(SCREAMING_SNAKE_CASE__	)

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												_SCREAMING_SNAKE_CASE					:			str							= os.path.join(
												    SCREAMING_SNAKE_CASE__				, weights_name.replace(""".bin"""				, f"""-{len(SCREAMING_SNAKE_CASE__	)+1:05d}-of-???.bin"""	)	)
												rename_and_save_block(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
												sharded_state_dicts.append(current_block.keys()	)
												del current_block
												_SCREAMING_SNAKE_CASE					:			str							= {}
												_SCREAMING_SNAKE_CASE					:			Any							= 0

								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= raw_weights.to(getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				_SCREAMING_SNAKE_CASE					:			Dict							= os.path.join(SCREAMING_SNAKE_CASE__				, weights_name.replace(""".bin"""				, f"""-{len(SCREAMING_SNAKE_CASE__	)+1:05d}-of-???.bin"""	)	)
				rename_and_save_block(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				sharded_state_dicts.append(current_block.keys()	)

				# If we only have one shard, we return it
				if len(SCREAMING_SNAKE_CASE__	) == 1:
								return {weights_name: sharded_state_dicts[0]}, None

				# Otherwise, let's build the index
				_SCREAMING_SNAKE_CASE					:			Dict							= {}
				_SCREAMING_SNAKE_CASE					:			str							= {}
				for idx, shard in enumerate(SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			List[str]							= weights_name.replace(
								    """.bin"""				, f"""-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE__	):05d}.bin"""	)  # len(sharded_state_dicts):05d}
								_SCREAMING_SNAKE_CASE					:			Any							= os.path.join(SCREAMING_SNAKE_CASE__				, weights_name.replace(""".bin"""				, f"""-{idx+1:05d}-of-???.bin"""	)	)
								os.rename(SCREAMING_SNAKE_CASE__				, os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= shard
								for key in shard:
												_SCREAMING_SNAKE_CASE					:			int							= shard_file

    # Add the metadata
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= {"""total_size""": total_size}
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= {"""metadata""": metadata, """weight_map""": weight_map}

				with open(os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)				, """w"""				, encoding="""utf-8"""	) as f:
								_SCREAMING_SNAKE_CASE					:			str							= json.dumps(SCREAMING_SNAKE_CASE__				, indent=2				, sort_keys=SCREAMING_SNAKE_CASE__	) + """\n"""
								f.write(SCREAMING_SNAKE_CASE__	)

				return metadata, index


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--switch_t5x_checkpoint_path',
				    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
				    type=str,
				    required=False,
				    help='Path to a directory containing a folder per layer. Follows the original Google format.',
				)
				parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
				parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
				    type=str,
				    required=False,
				    help='Path to the output pytorch model.',
				)
				UpperCAmelCase_						:  str				  =						parser.parse_args()
				shard_on_the_fly(
				    args.switch_tax_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.max_shard_size,
				    args.dtype,
				)







def      snake_case_  (    ):







				"""simple docstring"""





				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""	)
				config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= SwitchTransformersForConditionalGeneration.from_pretrained(
				    """/home/arthur_huggingface_co/transformers/switch_converted"""				, device_map="""auto"""	)

				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= TaTokenizer.from_pretrained("""t5-small"""	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

				_SCREAMING_SNAKE_CASE					:			List[Any]							= tokenizer(SCREAMING_SNAKE_CASE__				, return_tensors="""pt"""	).input_ids
				_SCREAMING_SNAKE_CASE					:			List[Any]							= model.generate(SCREAMING_SNAKE_CASE__				, decoder_start_token_id=0	)
				print(tokenizer.decode(out[0]	)	)








'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return "".join(chr(ord(SCREAMING_SNAKE_CASE__	) - 32	) if """a""" <= char <= """z""" else char for char in word	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




